Nontuberculous mycobacterial pulmonary disease (NTM PD) incidence trends in the United States, 2010–2019
Bibliographic record
Abstract
BACKGROUND: Nontuberculous mycobacteria (NTM) are ubiquitous environmental bacteria that cause chronic lung disease. Rates of NTM pulmonary disease (NTM PD) have increased over the last several decades, yet national estimates in the United States (US) have not been assessed since 2015. METHODS: We used a nationally representative population of Medicare beneficiaries aged ≥ 65 years to assess rates of NTM PD in a high-risk population from 2010 to 2019. Poisson generalized linear models were used to assess the annual percent change in incidence in the overall population and among key demographic groups such as sex, geography, and race/ethnicity. We evaluated the relative prevalence of various comorbid conditions previously found to be associated with NTM PD. RESULTS: We identified 59,724 cases of incident NTM PD from 2010 to 2019 from an annual mean population of 29,687,097 beneficiaries, with an average annual incidence of 20.1 per 100,000 population. NTM PD incidence was overall highest in the South and among women, Asian individuals, and persons aged ≥ 80 years relative to other studied demographic groups. The annual percent change in NTM PD incidence was highest in the Northeast, at 6.5%, and Midwest, at 5.9%, and among women, at 6.5%. Several comorbid conditions were highly associated with concurrent NTM diagnosis, including allergic bronchopulmonary aspergillosis, bronchiectasis, and cystic fibrosis. CONCLUSIONS: Here we provide current estimates of NTM PD incidence and prevalence and describe increasing trends in the US from 2010 to 2019. Our study suggests a need for improved healthcare planning to handle an increased future caseload, as well as improved diagnostics and therapeutics to better detect and treat NTM PD in populations aged ≥ 65 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".